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clovis.json
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"query":"What are the two components considered in the expected free energy?",
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"target_page":4,
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"target_passage":"The former (utilitarian) objective is to realize one’s preferences, such as being satiated or safe, by minimizing the discrepancy between preferred sensa- tions (encoded as “priors over observations” in active inference) and current sensations in different modalities (e.g. interoceptive or exteroceptive). The latter (epistemic) objective is to reduce uncertainty about one’s estimated state"
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"query":"What are the two components considered in the expected free energy?",
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"target_page":4,
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"target_passage":"The former (utilitarian) objective is to realize one’s preferences, such as being satiated or safe, by minimizing the discrepancy between preferred sensa- tions (encoded as “priors over observations” in active inference) and current sensations in different modalities (e.g. interoceptive or exteroceptive). The latter (epistemic) objective is to reduce uncertainty about one’s estimated state"
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},
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"source_file":"pubmed1.pdf",
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"query":"How could the heart rate be estimated by means of an active inference paradigm?",
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"target_page":6,
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"target_passage":"The second panel of Fig. 2 shows the Shannon surprise of an inference model that estimates the current heart rate using the two standard components of a generative model. The for- mer component is the prior, which encodes the person’s a priori probabilistic belief (i.e. probability distribution) about her “nor- mal” heart rate range; here, the prior is a Gaussian centered on 67 and has a precision of 0.11. The latter component is the likeli- hood, which encodes the probabilistic mapping between sensory (heartbeat) observations and the hidden state (heart rate); here, the likelihood is a Gaussian centered on the current heart rate with an additional bias of 15 pulses, and the panel shows the results for 10 values for precision obtained by subdividing the range [0.1,10] into equal intervals."
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}
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